Speaker: Dr. Ian T. Foster, University of Chicago and Argonne National Laboratory, USA​

Title of Talk: tba
Biography: Dr. Ian Foster is the Director of Argonne’s Data Science and Learning Division, Argonne Senior Scientist and Distinguished Fellow and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. He was the Director of Argonne’s Computation Institute from 2006 to 2016. Foster’s research contributions span high-performance computing, distributed systems, and data-driven discovery. He is widely recognized for co-inventing grid computing, which laid the groundwork for the cloud computing systems that are used today. He has published hundreds of scientific papers and eight books on these and other topics. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures. Foster received a BSc (Hons I) degree from the University of Canterbury, New Zealand, and a PhD from Imperial College, United Kingdom, both in computer science. Foster's honors include the Gordon Bell Prize for high-performance computing (2001), the Lovelace Medal of the British Computer Society (2002), the IEEE Tsutomu Kanai Award (2011), and the IEEE Charles Babbage Award (2019). He was elected Fellow of the British Computer Society in 2001, Fellow of the American Association for the Advancement of Science in 2003, and in 2009, a Fellow of the Association for Computing Machinery, who named him the inaugural recipient of the high-performance parallel and distributed computing (HPDC) achievement award in 2012. In 2017, he was recognized with the Euro-Par Achievement Award and in 2019 he was a recipient of the IEEE Computer Society Charles Babbage Award. 
Abstract: will be updated soon

Speaker: Dr. Arumugam Nallanathan, Queen Mary University of London, United Kingdom

Professor of Wireless Communications, IEEE Fellow, Web of Science Highly Cited Researcher (2016)

Title of Talk: Artificial Intelligence in Massive IoT Networks
Biography: Dr. Arumugam Nallanathan has been a Professor of wireless communications and the Head of the Communication Systems Research (CSR) Group, School of Electronics Engineering and Computer Science, Queen Mary University of London, since September 2017. He was with the Department of Informatics, King’s College London, from December 2007 to August 2017, where he was a Professor of wireless communications, from April 2013 to August 2017, and has been a Visiting Professor, since September 2017. He was an Assistant Professor with the Department of Electrical and Computer Engineering, National University of Singapore, from August 2000 to December 2007. He has published nearly 500 technical articles in scientific journals and international conferences. His research interests include artificial intelligence for wireless systems, beyond 5G wireless networks, the Internet of Things (IoT), and molecular communications.,Dr. Nallanathan is an IEEE Distinguished Lecturer. He was a Corecipient of the Best Paper Award presented from the IEEE International Conference on Communications (ICC), in 2016, the IEEE Global Communications Conference (GLOBECOM), in 2017, and the IEEE Vehicular Technology Conference (VTC), in 2018. He was selected as a Web of Science Highly Cited Researcher, in 2016. He has served as the Chair of the Signal Processing and Communication Electronics Technical Committee of the IEEE Communications Society, and the technical program chair and a member of technical program committees of numerous IEEE conferences. He received the IEEE Communications Society’s SPCE Outstanding Service Award, in 2012, and the IEEE Communications Society’s RCC Outstanding Service Award, in 2014. He was an Editor of the IEEE Transactions on Wireless Communications, from 2006 to 2011, the IEEE Transactions on Vehicular Technology, from 2006 to 2017, the IEEE Wireless Communications Letters, and the IEEE Signal Processing Letters. He is an Editor of the IEEE Transactions on Communications. (Based on document published on 11 June 2020). More information at http://www.eecs.qmul.ac.uk/~nalla/ . 
Abstract: will be updated soon

Speaker: Dr. Mohammed Atiquzzaman, Edith J. Kinney Gaylord Presidential Professor, University of Oklahoma, USA 

Title of Talk: Connected Autonomous Vehicles
Biography: Mohammed Atiquzzaman obtained his M.S. and Ph.D. in Electrical Engineering and Electronics from the University of Manchester (UK) in 1984 and 1987, respectively.  He currently holds the Edith J Kinney Gaylord Presidential professorship in the School of Computer Science at the University of Oklahoma. Dr. Atiquzzaman is the Editor-in-Chief of Journal of Networks and Computer Applications, the founding Editor-in-Chief of Vehicular Communications, and serves/served on the editorial boards of many journals including IEEE Communications Magazine, Real Time Imaging Journal, International Journal of Communication Networks and Distributed Systems and Journal of Sensor Networks and International Journal of Communication Systems. He co-chaired the IEEE High Performance Switching and Routing Symposium (2003, 2011), IEEE Globecom and ICC (2014, 2012, 2010, 2009, 2007, 2006), IEEE VTC (2013)  and the SPIE Quality of Service over Next Generation Data Networks conferences (2001, 2002, 2003). He was the panels co-chair of INFOCOM’05, and is/has been in the program committee of many conferences such as INFOCOM, Globecom, ICCCN, ICCIT, Local Computer Networks, and serves on the review panels at the National Science Foundation. 

Dr. Atiquzzaman received IEEE Communication Society's Fred W. Ellersick Prize, IEEE Distinguished Technical Achievement Award for contributions in switching and routing, IEEE Satellite Communications Technical Contribution Award, and NASA Group Achievement Award for "outstanding work to further NASA Glenn Research Center's effort in the area of Advanced Communications/Air Traffic Management's Fiber Optic Signal Distribution for Aeronautical Communications" project. He is the co-author of the book “Performance of TCP/IP over ATM networks” and has over 300 refereed publications, available at www.cs.ou.edu/~atiq.

His current research interests are in areas of Internet of Things, wireless and mobile networks, ad hoc networks, satellite networks, vehicular communications, and optical communications. His research has been funded by over $10M from National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), U.S. Air Force, Cisco, Honeywell, Oklahoma Department of Transportation and Oklahoma Highway Safety Office. More information at https://www.cs.ou.edu/~atiq/

Abstract: Modern vehicles are equipped with lots of sensors for measurement of vehicle operating conditions and the surrounding, including weather conditions, and can be a viewed as a web of sensors on wheels. They can sense a range of information about the vehicle, such as location, speed, braking intensity, road traction, etc., some of which can represent road weather conditions.  Lots of crashes happen due to the driver being unware of the surrounding road weather conditions, such as icy patches and frozen pavement. By facilitating vehicles within an area to exchange information between themselves in real-time, the drivers can be instantly alerted about road hazards and possibly avoid potential crashes.  The talk will discuss ways to increase the safety of drivers and thus reduce crashes resulting from adverse road weather conditions. This was achieved by disseminating, in real-time, the information collected by a vehicle to its surrounding vehicles using state-of-the-art wireless communications between vehicles. The information was also communicated to road side infrastructure to increase driver safety; for example, the duration of the traffic signals at a junction can be changed dynamically in response to current road weather conditions transmitted by vehicles in the surrounding area.

Speaker: Dr. Vipin Chaudhary, Kevin J. Kranzusch Professor and Chair,  Case Western Reserve University, USA

Title of Talk: Predictive Analytics Frameworks for Forecasting High Impact Economic Impacts and Insider Trading Events​
Biography: Vipin Chaudhary is the Kevin J. Kranzusch Professor and Chair of the Department of Computer and Data Sciences at Case Western Reserve University. Most recently, he was a Program Director at the National Science Foundation where he was involved in many national initiatives and the Empire Innovation Professor of Computer Science and Engineering at SUNY Buffalo. He cofounded Scalable Informatics, a leading provider of pragmatic, high performance software-defined storage and compute solutions to a wide range of markets, from financial and scientific computing to research and big data analytics. From 2010 to 2013, Dr. Chaudhary was the Chief Executive Officer of Computational Research Laboratories (CRL), a wholly owned Tata Sons company, where he grew the company globally to be an HPC cloud and solutions leader before selling it to Tata Consulting Services. Prior to this, as Senior Director of Advanced Development at Cradle Technologies, Inc., he was responsible for advanced programming tools for multi-processor chips. He was also the Chief Architect at Corio Inc., which had a successful IPO in July, 2000. Dr. Chaudhary was awarded the prestigious President of India Gold Medal in 1986 at the Indian Institute of Technology (IIT) Kharagpur where he received the B.Tech. (Hons.) degree in Computer Science and Engineering and a Ph.D. degree from The University of Texas at Austin.
Abstract: Financial markets are driven by complex dynamics and interplay, often stemming from convoluted investor interactions, asset and inter-market complexities. Recent financial market events such as the sub-prime mortgage crisis of 2008, have necessitated the need for the development of strategies to deal with the acute stresses of renewed economic uncertainties, monitor systemic activities, and generate actionable intelligence. Despite several advancements, the modeling of financial markets events remains elusive due to complex interactions among the market constituents. In this talk I will suggest various predictive models to forecast high impact economic events and mine illegal trading activities driven by material non-public information. Our results on real test data confirm the efficacy of the proposed solutions to forecast economic recessions over multiple horizon periods and to detect insider trading activities in the U.S. equity markets.

Speaker: Dr. Dilip Krishnaswamy, Vice President (New Technology R&D), Reliance Industries Ltd, India 

Title of Talk: Quantum-inspired Processing and Networking
Biography: Dr. Dilip Krishnaswamy is currently serving as Vice President (New Technology Initiatives) at Reliance Industries Ltd. He has a Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign. Previously, he has worked as a Senior Scientist at IBM Research, as a Senior Staff Researcher in the office of the Chief Scientist at Qualcomm, and as a Platform Architect at Intel, and has taught at the University of California, Davis. He is an inventor on 60+ granted US patents, and has 70+ publications, with 3 best paper awards. His research interests include distributed information processing, distributed function virtualization, distributed resource management, block chain technology, edge services, NFV/SDN, NB-IoT, AI/ML, and 5G architecture & systems. He served as the Associate Editor-in-Chief of IEEE Wireless Communications from 2009-2014. He recently chaired the Pat Goldberg CS/EE/Math best paper competition for IBM Research worldwide (late 2014 to early 2018). He is a B. Tech. (Electronics and Communications Engineering) alumnus of IIT Madras. More information at https://sites.google.com/site/dilip1/
Abstract:  This talk will provide an introduction to quantum physics and will also introduce quantum computing-based processing based on matrix unitary transformations. The talk will also suggest possibilities for quantum-inspired processing, and discuss some recent work in the area of quantum blockchain networks. 

Speaker: Dr. Jemal H. Abawajy, Deakin University, Australia

Title of Talk: Big Data Graph Dimensionality Challenges and Solutions
Biography: Dr. Jemal H. Abawajy is a full professor at Faculty of Science, Engineering and Built Environment, Deakin University, Australia. He is a Senior Member of IEEE Society; IEEE Technical Committee on Scalable Computing (TCSC); IEEE Technical Committee on Dependable Computing and Fault Tolerance and IEEE Communication Society. His leadership is extensive spanning industrial, academic and professional areas (e.g., IEEE Technical Committee on Scalable Computing, Academic Board, Faculty Board and Research Integrity Advisory Group). Professor Abawajy has delivered numerous keynote addresses, invited seminars, and media briefings (e.g., Voice of America's English Radio). He has been actively involved in the organisation of more than 400 national and international conferences in various capacity including chair, general co-chair, vice-chair, best paper award chair, publication chair, session chair and program committee. Professor Abawajy has served on the editorial-board of numerous international journals and currently serving as associate editor of the IEEE Transaction on Cloud Computing, International Journal of Big Data Intelligence and International Journal of Parallel, Emergent and Distributed Systems. He has also guest edited many special issue journals. He is the author/coauthor of more than 250 refereed articles and supervised numerous Ph.D. students to completion.  More information at https://personal-sites.deakin.edu.au/~jemal/. 
Abstract: Graphs, with more expressive power and rich analytic abilities, have increasingly become prevalent in a variety of emerging application in business, science and engineering domains. Although big graph data provides tremendous flexibility in representing highly interrelated data as well as easily connect diverse types of related information, it poses a number of serious challenges ranging from efficient processing to security and privacy issues. In this presentation, we will discuss at some of the applications domains that use graphs. We will also discuss various challenges and some approaches we have developed to address these challenges. 

Speaker: Prof. Sri Krishnan, Ryerson University, Toronto, Ontario, Canada

Title of Talk: Signal Analysis for Remote Health Monitoring
Biography: Sri Krishnan received the B.E. degree in Electronics and Communication Engineering from the College of Engineering, Guindy, Anna University, India, in 1993, and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Calgary, Calgary, Alberta, Canada, in 1996 and 1999 respectively. He joined the Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada in July 1999, and currently he is a Professor in the Department. Since July 2011, he is an Associate Dean (Research and Development) for the Faculty of Engineering and Architectural Science. He is also the Founding Co-Director of the Institute for Biomedical Engineering, Science and Technology (iBEST) and an Affiliate Scientist at the Keenan Research Centre in St. Michael's Hospital, Toronto. iBEST is a research and innovation partnership between Ryerson University and St. Michael's Hospital which includes more than 37 scientists/engineers and 110 students/trainees from both the institutions with the mandate of bench to bedside discovery research to translational outcomes.

Sri Krishnan held the Canada Research Chair position (2007-2017) in Biomedical Signal Analysis. He serves in the editorial boards of Biomedical Signal Processing and Control journal and Sensors journal. Sri Krishnan is also a technical committee member of Biomedical Signal Processing in the IEEE Engineering in Medicine and Biology Society. He is a Fellow of the Canadian Academy of Engineering and a registered professional engineer in the Province of Ontario.

Abstract: In this talk, the contextual topic of remote health monitoring using wearable devices, information and communication technology, signal processing and machine learning will be covered. Home-based remote monitoring of vital health signals will not only benefit the current pandemic situation but also long term healthcare needs such as telemedicine, digital health and rehabilitation. The talk will also cover the current research done in the area of connected healthcare and wearable computing in the Signal Analysis Research Group at Ryerson University, Canada.

Speaker: Dr. Stefano Berretti, University of Florence, Italy

Title of Talk: 3D Face Modeling​
Biography: Stefano Berretti received his Master and PhD degrees in Computer Engineering from the University of Florence, Italy, where he is currently an Associate Professor at the Media Integration and Communication Center and at the Department of Information Engineering. He was a visiting researcher at the Indian Institute of Technology (IIT) in Mumbai, at the University of Lille, and at the Khalifa University. More recently, he has been also Visiting Professor at the University of Lille, and the University of Edmonton. He is also the Information Director and an Associate Editor of the ACM Transactions on Multimedia Computing, Communications, and Applications(ACM TOMM), and an Associate Editor of the IET Computer Vision journal. Stefano is interested in the combination of computer vision, machine learning and graphics to investigate new methods for reconstruction, modelling and recognizing human faces and expression in 3D, for human emotion and behavior understanding from 2D and 3D images and videos. He is also interested in manifold-based methods for modeling and recognition in non-Euclidean domains.
Abstract: Though 3D Morphable Models (3DMM) of the face dates to late ’90, in the past few years they have been re-discovered in the context of deep learning and are now incorporated into many state-of-the-art solutions for face analysis. In this talk, I will discuss recent approaches to the problem of 3DMM construction and fitting, focusing on the challenges in building and applying these models, and the main insights that helped us address them. I will additionally mention interesting open problems, highlighting the broad range of current and future applications, and some stepping stones towards unexplored directions.

Speaker: Prof. Ljiljana Trajkovic, Simon Fraser University, Burnaby, British Columbia, Canada

Title of Talk: Detecting network anomalies and intrusions
Biography: Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, in 1974, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, in 1979 and 1981, respectively, and the Ph.D. degree in electrical engineering from University of California at Los Angeles, in 1986. She is currently a Professor in the School of Engineering Science at Simon Fraser University, Burnaby, British Columbia, Canada. From 1995 to 1997, she was a National Science Foundation (NSF) Visiting Professor in the Electrical Engineering and Computer Sciences Department, University of California, Berkeley. She was a Research Scientist at Bell Communications Research, Morristown, NJ, from 1990 to 1997, and a Member of the Technical Staff at AT&T Bell Laboratories, Murray Hill, NJ, from 1988 to 1990. Her research interests include high-performance communication networks, control of communication systems, computer-aided circuit analysis and design, and theory of nonlinear circuits and dynamical systems.
Dr. Trajkovic serves as IEEE Division X Delegate/Director (2019-2020) and served as IEEE Division X Delegate-Elect/Director-Elect (2018). She served as Senior Past President (2018-2019), Junior Past President (2016-2017), President (2014-2015), President-Elect (2013), Vice President Publications (2012-2013, 2010-2011), Vice President Long-Range Planning and Finance (2008-2009), and a Member at Large of the Board of Governors (2004-2006) of the IEEE Systems, Man, and Cybernetics Society. She served as 2007 President of the IEEE Circuits and Systems Society and a member of its Board of Governors (2004-2005, 2001-2003). She is Chair of the IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections. She was Chair of the IEEE Technical Committee on Nonlinear Circuits and Systems (1998). She is General Co-Chair of SMC 2020 and SMC 2020 Workshop on BMI Systems and served as General Co-Chair of SMC 2019 and SMC 2018 Workshops on BMI Systems, SMC 2016, and HPSR 2014, Special Sessions Co-Chair of SMC 2017, Technical Program Chair of SMC 2017 and SMC 2016 Workshops on BMI Systems, Technical Program Co-Chair of ISCAS 2005, and Technical Program Chair and Vice General Co-Chair of ISCAS 2004. She served as an Associate Editor of the IEEE Transactions on Circuits and Systems (Part I) (2004-2005, 1993-1995), the IEEE Transactions on Circuits and Systems (Part II) (2018, 2002-2003, 1999-2001), and the IEEE Circuits and Systems Magazine (2001-2003). She is a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society (2020-2021) and the IEEE Circuits and Systems Society (2010-2011, 2002-2003). She is a Professional Member of IEEE-HKN and a Life Fellow of the IEEE..
Abstract: The Internet, social networks, power grids, gene regulatory networks, neuronal systems, food webs, social systems, and networks emanating from augmented and virtual reality platforms are all examples of complex networks. Collection and analysis of data from these networks is essential for their understanding. Traffic traces collected from various deployed communication networks and the Internet have been used to characterize and model network traffic, analyze network topologies, and classify network anomalies. Data mining and statistical analysis of network data have been employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while spectral graph theory has been applied to analyze network topologies and capture historical trends in their development. Machine learning techniques have proved valuable for predicting anomalous traffic behavior and for classifying anomalies and intrusions in communication networks. Applications of these tools help understand the underlying mechanisms that affect behavior, performance, and security of computer networks.

Speaker: Dr. Juan Manuel Corchado, Director - European IoT Digital Innovation Hub, Director- BISITE Research Group, University of Salamanca & President of the Air Institute, Spain

Title of Talk: Efficient Deployment of DeepTech AI Models in Engineering Solutions
Biography: Juan Manuel Corchado (born May 15, 1971 in Salamanca, Spain). He is Full Professor with Chair at the University of Salamanca. He was Vice President for Research and Technology Transfer from December 2013 to December 2017 and the Director of the Science Park of the University of Salamanca, Director of the Doctoral School of the University until December 2017 and also, he has been elected twice as the Dean of the Faculty of Science at the University of Salamanca. In addition to a PhD in Computer Sciences from the University of Salamanca, he holds a PhD in Artificial Intelligence from the University of the West of Scotland. Juan Manuel Corchado is Visiting Professor at Osaka Institute of Technology since January 2015 and Visiting Professor at the Universiti Malaysia Kelantan.
Corchado is the Director of the European IoT Digital Innovation Hub and of the BISITE (Bioinformatics, Intelligent Systems and Educational Technology) Research Group, which he created in the year 2000, President of the AIR Institute, Academic Director of the Institute of Digital Art and Animation of the University of Salamanca and has been President of the IEEE Systems, Man and Cybernetics Spanish Chapter. He also oversees the Master´s programs in Digital Animation, Security, Blockchain, IoT, Mobile Technology, Information Systems Management and Agile Project Management at the University of Salamanca.
Corchado has supervised more than 25 PhD theses, is author of over 800 research peer review papers and books, has chaired the scientific committee of more than 30 international conferences, and is also Editor-in-Chief of Specialized Journals like ADCAIJ (Advances in Distributed Computing and Artificial Intelligence Journal) and OJCST (Oriental Journal of Computer Science and Technology)..
Abstract: Artificial Intelligence revived in the last decade. The need for progress, the growing processing capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to today’s problems. Nevertheless, our success is not entirely based on algorithms, it also comes from our ability to follow our “gut” when choosing the best combination of algorithms for an intelligent artefact. It's about approaching engineering with a lot of knowledge and tact. This involves the use of both connectionist and symbolic systems, and of having a full understanding of the algorithms used. Moreover, to address today’s problems we must work with both historical and real-time data. We must fully comprehend the problem, its time evolution, as well as the relevance and implications of each piece of data, etc.It is also important to consider development time, costs and the ability to create systems that will interact with their environment, will connect with the objects that surround them and will manage the data they obtain in a reliable manner.

In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems.It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively."Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities. The use of edge platforms or fog computing helps increase efficiency, reduce network latency, improve security and bring intelligence to the edge of the network;close to the sensors, users and to the medium used.

This keynote will present success stories regarding biotechnology, smart cities, industry 4.0, the economy, and others. All these fields require the development of interactive, reliable and secure systems which we are capable of building thanks to current advances.Several use cases of intelligent systems will be presented and it will be  the different processes have been optimized by means of tools that facilitate decision-making.

Speaker: Dr. Arpan Pal, Chief Scientist and Research Area Head, Embedded Systems and Robotics, TCS Research, India

Title of Talk: Device Edge Computing: Next Frontiers for IoT and Robotics​
Biography: Arpan Pal received both his B.Tech and M.Tech from Indian Institute of Technology, Kharagpur, India in Electronics and Telecommunications and PhD. from Aalborg University Denmark. He has more than 27 years of experience in the area of Signal Processing, Communication, Embedded Systems and Robotics. Currently he is with Tata Consultancy Services (TCS), where, as Chief Scientist, he is heading the Embedded Systems and Robotics Research Area in TCS Research. Prior to joining TCS, Arpan has led the real-time Systems group in Macmet Interactive Technology Pvt. Ltd. and had been involved in design / development of missile seeker signal processors in Defense Research and Development Organization (DRDO), Govt. of India. His research interests include Sensing & IoT, Signal Processing & AI, Robotics & Edge Processing. He has contributed in research, development and deployment of embedded sensing and control systems products in areas of Telecommunication Systems, Missile Systems, Interactive Television, Internet of Things, Robotics and AI driven analytics. Arpan has more than 125 papers and book chapters till date in reputed Journals and Conferences. He has also authored a complete book on IoT. He has filed for more than 150 patents and has more than 100 patents granted to him in different geographies.  He had been on the editorial board for reputed journals like ACM Transactions on Embedded Computing Systems, IEEE Transactions on Emerging Topics in Computing and Springer-Nature Journal on Computing Systems. He is a Senior Member of IEEE and is engaged in the innovation space in different industry bodies like NASSCOM, CII, BCCI and various start-up accelerators.  He is on the review board of various Govt. initiatives like IMPRINT, CSIR Mission mode initiative etc.
Abstract: Edge computing is the next frontier for IoT where analytics and AI needs to be performed in the Edge device itself without sending the sensor data to the cloud. In this talk we will discuss the main drivers for device edge computing, outline the application use cases and provide a glimpse of the required technology – current and future.

Speaker: Dr. Danda B. Rawat, Howard University, Washington, DC, USA

Title of Talk: Secure and Trustworthy Machine Learning and Artificial Intelligence for Emerging Systems and Applications: The Triumph and Tribulation
Biography: Dr. Danda B. Rawat is a Full Professor in the Department of Electrical Engineering & Computer Science (EECS), Founder and Director of the Howard University Data Science and Cybersecurity Center, Director of Cyber-security and Wireless Networking Innovations (CWiNs) Research Lab, Graduate Program Director of Howard CS Graduate Programs and Director of Graduate Cybersecurity Certificate Program at Howard University, Washington, DC, USA. Dr. Rawat is engaged in research and teaching in the areas of cybersecurity, machine learning, big data analytics and wireless networking for emerging networked systems including cyber-physical systems, Internet-of-Things, multi domain battle, smart cities, software defined systems and vehicular networks. His professional career comprises more than 18 years in academia, government, and industry. He has secured over $16 million in research funding from the US National Science Foundation (NSF), US Department of Homeland Security (DHS), US National Security Agency (NSA), US Department of Energy, National Nuclear Security Administration (NNSA), DoD and DoD Research Labs, Industry (Microsoft, Intel, etc.) and private Foundations. Dr. Rawat is the recipient of NSF CAREER Award in 2016, Department of Homeland Security (DHS) Scientific Leadership Award in 2017, Researcher Exemplar Award 2019 and Graduate Faculty Exemplar Award 2019 from Howard University, the US Air Force Research Laboratory (AFRL) Summer Faculty Visiting Fellowship in 2017, Outstanding Research Faculty Award (Award for Excellence in Scholarly Activity) at GSU in 2015, the Best Paper Awards (IEEE CCNC, IEEE ICII, BWCA) and Outstanding PhD Researcher Award in 2009. He has delivered over 20 Keynotes and invited speeches at international conferences and workshops. Dr. Rawat has published over 200 scientific/technical articles and 10 books. He has been serving as an Editor/Guest Editor for over 50 international journals including the Associate Editor of IEEE Transactions of Service Computing, Editor of IEEE Internet of Things Journal, Associate Editor of IEEE Transactions of Network Science and Engineering and Technical Editors of IEEE Network. He has been in Organizing Committees for several IEEE flagship conferences such as IEEE INFOCOM, IEEE CNS, IEEE ICC, IEEE GLOBECOM and so on. He served as a technical program committee (TPC) member for several international conferences including IEEE INFOCOM, IEEE GLOBECOM, IEEE CCNC, IEEE GreenCom, IEEE ICC, IEEE WCNC and IEEE VTC conferences. He served as a Vice Chair of the Executive Committee of the IEEE Savannah Section from 2013 to 2017. Dr. Rawat received the Ph.D. degree from Old Dominion University, Norfolk, Virginia. Dr. Rawat is a Senior Member of IEEE and ACM, a member of ASEE and AAAS, and a Fellow of the Institution of Engineering and Technology (IET).
Abstract: This keynote focuses on both AI for cybersecurity and cybersecurity for AI for emerging systems and applications. Lately, ML algorithms and AI systems have been shown to be able to create machine cognition comparable to or even better than human cognition for some applications. Machine learning algorithms are now regarded as very useful cybersecurity solutions for different emerging applications. However, because ML algorithms and AI systems can be controlled, dodged, biased, and misled through flawed learning models and input data, they need robust security features and trustworthy AI. It is very important to design and evaluate/test ML algorithms and AI systems that produce reliable, robust, trustworthy, explainable and fair/unbiased outcomes to make them acceptable by diverse users. The keynote covers applications and use cases of secure and trustworthy ML/AI and their success and pitfalls.

Speaker: Dr. Nicolas Sklavos, University of Patras, Hellas

Title of Talk: In Hardware We Trust: Electronic Design Automation
Biography: Dr. Nicolas Sklavos is Associate Professor, in Computer Engineering and Informatics Department (CEID), Polytechnic School, University of Patras, Hellas. He is Director of SCYTALE Group. His research interests include Cryptographic Engineering, Hardware Security, Cyber Security, Digital Systems Design, and Embedded Systems. He has participated to a number of European/National, Research and Development Projects. He has received several scientific awards in the related areas of his research. He has participated to the organization of international scientific conferences, of IEEE/ACM/IFIP, serving several committee duties, as well as Editorial Board Member of Scientific Journals. He has authored technical papers, books, chapters, reports etc, in the areas of his research. His published works has been cited in several papers of other authors, in technical literature. He is Senior Member of IEEE, Associated Member of HiPEAC and member of IACR. His works, have received a great number of references, in scientific, technical literature. (Homepage: http://www.scytale.ceid.upatras.gr)..
Abstract: Modern handheld devices and systems are developed day by day, in order to satisfy the complexity of users’ needs and applications. Nowadays, integrated circuits (ICs) play a sensitive role in devices’ operation, since they are the main cores for almost each type of process and data transaction. The needs for high performance, minimized area, and less power, are more demanding each time, and electronic design automation (EDA), is oriented as a crucial factor, for these targets. Although, besides the traditional circuits and systems, design approaches, the arising threats in hardware each time, make very important the priority for secure hardware design, and trusted devices, at the same time. Traditional approaches of design and test, are argued, since most of the processes’ parts, need considerations, assumptions and specifications, for both trustworthy and security in all metrics, including modeling and evaluation. This keynote talk, gives a detailed overview of hardware security and EDA approaches, including security threats, in integrated circuits, though the design cycle. It also, deals with, the countermeasures and the motivation of the prior art. Examples of modern applications are introduced, in sense of trusted hardware, and secure by design. Solutions, and alternative approaches are figured out, as well, detailed overview is discussed, for the expectations of future, for both users’ applications, and devices.

Speaker: Dr. El-Sayed El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia

Title of Talk: Learning from Class-Imbalanced Data: Challenges,Methods and Applications​
Biography: El-Sayed M. El-Alfy, Professor King Fahd Univ. of Petroleum and Minerals (KFUPM). He has 25+ years of experience in industry and academia involving research, teaching, supervision, curriculum design, program assessment and quality assurance. He is an active researcher in machine learning and nature-inspired computing and applications to data science and cyber analytics, pattern recognition, multimedia forensics, and security systems. He published numerously in peer-reviewed int’l journals and conferences, edited a number of books, contributed to organization of many int’l conferences, served as guest editor for a number of special issues, and been in editorial board of a number of premium journals including IEEE/CAA Journal of AutomaticaSinica, IEEE Transactions on Neural Networks and Learning Systems, International Journal on Trust Management in Computing and Communications, and Journal of Emerging Technologies in Web Intelligence (JETWI). He co-founded and coordinated a research group on Intelligent Systems at KFUPM. He is a member of IEEE Computational Intelligence Society, and x-member of ACM and IEEE Computer Society. His work has been internationally recognized and received several awards.
Abstract: Nowadays, machine learning and intelligent systems are gaining increasing importance in this era of digital transformation.As more data is generated, the advances in this field present new opportunities in a wide spectrum of domains such as healthcare, finance, social media, cybersecurity, industrial systems, and sensor networks. However, some events or classes are rare and not equally represented in data for many real-world applications. This imposes several challenges for standard machine learning classification algorithms. Though several approaches have been proposed over the past decades, there are open issues that need further investigation. In this talk, we review majorresearch challenges and state-of-the-art solutions with examples for handling imbalanced datasets in order to build more effective models.

Speaker: Dr. Axel Sikora, University of Applied Sciences Offenburg, Germany 

Title of Talk: AI Approaches for IoT Security Analysis​​
Biography: Axel Sikora holds a master (M.Sc. / Dipl.-Ing.) of Electrical Engineering and a master of Business Administration (MBA, Dipl. Wirt-Ing.), both from Aachen Technical University, Germany. He is a DAAD alumnus from 1990/91 in St Petersburg Politechnical Institute. He has done a Ph.D. (Dr.-Ing.) in Electrical Engineering at the Fraunhofer Institute of Microelectronics Circuits and Systems, Duisburg, with a thesis on SOI-technologies. After various positions in the telecommunications and semiconductor industry, he became a professor at the Baden-Wuerttemberg Cooperative State University Loerrach in 1999. In 2011, he joined Offenburg University of Applied Sciences, where he now leads the Institute of Reliable Embedded Systems and Communication Electronics (ivESK). Since Jan 2016, he is also deputy member of the board to Hahn-Schickard Association of Applied Research, one of the state-funded research institutes in Baden-Wuerttemberg, where he now leads two engineering divisions "Embedded Solutions" and "Software Solutions". Since October 2019, he is also affiliated professor to Technical Faculty of Freiburg University. His major interest is in the field of efficient, energy-aware, autonomous, secure and value-added algorithms and protocols for wired and wireless embedded communication with a strong focus on primary communication, gateway solutions, and data analytics for cyber-physical systems. Dr. Sikora is founder and shareholder of STACKFORCE GmbH, an independent and successul spin-off engineering company around IoT connectivity solutions. He is author, co-author, and editor and coeditor of several textbooks and more than 250 papers in the field of embedded design and wireless & wired networking. Amongst many other duties, he serves as Chairman of the annual embedded world Conference (Nuremberg), the world's largest event on the topic.
Abstract: IoT networks are increasingly used as entry points for cyber attacks, as often they offer low security levels, as they may allow the control of physical systems, and as they potentially also open the access to other IT networks and infrastructures. Existing Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) mostly concentrate on legacy IT networks. Nowadays, they come with a high degree of complexity and adaptivity, including the use of Artificial Intelligence (AI) and Machine Learning (ML). It is only recently, that these techniques are also applied to IoT networks. The keynote gives on overview of the state of the art of IoT network security and about AI-based approaches for the IoT security analysis.

Speaker: Dr. Domenico Ciuonzo, DIETI, University of Naples, Federico II, Italy

Title of Talk: Toward effective Network Traffic Classification via Deep Learning
Biography: Domenico Ciuonzo is an Assistant Professor at DIETI, University of Naples, Federico II, Italy. He received the B.Sc. and M.Sc. (summa) degrees in computer engineering and the Ph.D. degree from the University of Campania "L. Vanvitelli", Aversa, Italy, in 2007, 2009, and 2013, respectively. Since 2011, he has held several visiting appointments: NATO CMRE, IT (2011); ECE Department, University of Connecticut, US (2012); Department of Electronics and Telecommunications, NTNU, Trondheim, NOR (2015 and 2016); Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Castelldefels, ES (2018). His reviewing activities were recognized by the IEEE Communications Letters (in '13, '17 and '19), IEEE Trans. on Communications (in '14), IEEE Trans. on Instrumentation and Measurement (in '16), IEEE Transactions on Wireless Communications (in '17 and '18) and MDPI Sensors (in '17), which nominated him Exemplary Reviewer. He also received a similar recognition (“Top Reviewers” Award) for the whole MDPI publisher in 2017. Furthermore, his editorial activities were recognized by the IEEE Communications Letters, which nominated him Best Editor in '18 and '19, respectively. Since ‘14 he has served as Associate Editor for several IET, Elsevier and IEEE journals. Currently, he is also an Area Editor for the IEEE Transactions on Aerospace and Electronic Systems and the IEEE Communications Letters. His research interests fall within the areas of data fusion, network traffic analysis, statistical signal processing, IoT and wireless sensor networks and wireless communications. Domenico Ciuonzo has co-authored 85+ journal and conference publications within highly-reputed venues. Since 2016 he is an IEEE Senior Member. In '19, he received the Best Paper Award at 4th IEEE ICCCS. In '19, he was the recipient of the "Exceptional Service Award", from IEEE Aerospace and Electronic Systems Society (AESS).  In '20 he received the "Technical Achievement Award", from IEEE Sensors Council for the area Sensor Systems or Networks (early career). In the same year, he received Best Paper Award from Elsevier Computer Networks. He is co-author of the book “Data Fusion in Wireless Sensor Networks: A Statistical Perspective”, published by the IET (Apr. 2019). D. Ciuonzo has served and serves as independent reviewer/evaluator of research and implementation projects and project proposals co-funded by many EU and non-EU parties.
Abstract: In recent years operators have experienced the tremendous growth of the traffic to be managed in their networks, whose heterogeneous composition (e.g. mobile/IoT devices, anonymity tools), dynamicity, and increasing encryption is posing new challenges toward actionable network traffic analytics. In this talk, the topic of network traffic classification will be covered, due to its applications in network management, user-tailored experience, and privacy. First, the reasoned use of the Deep Learning umbrella will be introduced and explained in such a context. Hence, lessons learned and common pitfalls will be highlighted. Subsequently, the adoption of sophisticated multi-modal multi-task architectures will be put forward.  The talk will also cover the current research done in the area of AI-based network traffic analysis at the TRAFFIC group of the University of Naples Federico II, Italy.

Speaker: Dr. Marcin Paprzycki, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Title of Talk: Towards edge-fog-cloud continuum
Biography: Dr. Marcin PAPRZYCKI has an MS degree from the Adam Mickiewicz University in Poznań, Poland, a Ph.D. from the Southern Methodist University in Dallas, Texas, USA, and a Doctor of Science degree from the Bulgarian Academy of Sciences, Sofia, Bulgaria. He is a Senior Member of IEEE, a Senior Member of ACM, a Senior Fulbright Lecturer, and was an IEEE CS Distinguished Visitor. His original research interests were in the area of high performance computing / parallel computing / computational mathematics. Over time they shifted towards intelligent systems, software agents and agent systems, and application of semantic technologies, among others. Currently he serves as Vice Chair of the IEEE Poland Section. He has contributed to more than 500 publications, and was invited to the program committees of over 800 international conferences. He is on the editorial boards of 12 journals.
Abstract: Over time, two trends have been observed in the "world of computing". One of them was a push from centralized towards decentralized solutions. The second was the move in the opposite direction. These seem to be similar to the thesis and antithesis in Hegel's philosophy. Interestingly, similarly to Hegel's synthesis, we are approaching a unified model of edge-fog-cloud continuum. My talk will reflect on the journey and outline the proposed way forward.

Speaker: Dr. Jose Joseph, Indian Institute of Information Technology and Management-Kerala (IIITM-K), India

Title of Talk: Smart Sensors
Biography: Jose Joseph is an Assistant Professor at Indian Institute of Information Technology and Management-Kerala (IIITM-K). Prior to joining IIITM-K, he was working as a post-doctoral researcher in E. L. Ginzton laboratory (Electrical Engineering) at Stanford University, CA, USA. He also worked as a research associate in Center for Nanoscience and Engineering (CeNSE) & Mechanical Engineering, Indian Institute of Science (IISc), Bangalore. He earned his Ph.D in Electrical Engineering from Indian Institute of Technology (IIT), Hyderabad. In his research career, Jose focused on developing novel ultrasound transducers for imaging, biometric and airborne applications. He also worked on developing miniaturized biosensors and sensors for precision agriculture. He has seven years of microfabrication experience. His primary research interest is applied ultrasonics (neuromodulation, fingerprint detection, airborne navigation etc.).
Abstract: Sensors and sensor systems have been prevalent in commercial, industrial, healthcare, and military applications over the past several decades. The addition of ‘smartness’ to sensors made them to ‘think’ and ‘act’ according to the situation. This talk analyzes the technological aspects of incorporating smartness to the sensors. Contributions of few of the process innovations such as monolithic integration and 3D IC, in converting sensors to smart sensors, will be covered superficially. The talk will try to through light on the topic with a slight inclination towards electronic and microfabrication aspects.